Where you are: you use GenAI daily and the output is usually good enough. The gap: the layer that decides quality, latency and cost — Generative AI, prompt engineering, NLP, deep learning, transformer architecture, BERT, NER fine-tuning, text generation, MLOps, FastAPI, Docker, Kubernetes, SageMaker. Where this takes you: you choose an approach, fine-tune for a real task, and defend the trade-off in a design review. The path: 18 sections, in the order the concepts actually build.
GenAI tooling lets you get results without knowing what produced them. That holds until someone asks why the model behaves this way, what it costs at volume, or which variant fits the task. Here is where those questions usually land.
Preprocessing, tokenization and classical representations got skipped on the way to the chat UI.
Neural networks, activation functions, initialization and regularization are recall, not working knowledge.
Self-attention, encoder/decoder blocks, positional information — conceptually present, structurally unclear.
Everyone can name the paper. Far fewer can draw what the mechanism does to a sequence.
Different transformer variants get used interchangeably without understanding when each one fits.
Adapting a transformer to a real downstream task sits well outside a prompt-only workflow.
Greedy search, beam search and their trade-offs get chosen by default rather than on purpose.
Model packaging, FastAPI serving and monitoring are not yet part of how your work reaches users.
Orchestration and managed AI platforms have been read about, not worked through end to end.
Prompting is a skill, not a foundation. Once you can reason about NLP pipelines, transformer behaviour and production workflows, Agentic AI, RAG and LLMOps stop being vocabulary and start being engineering decisions you can argue.
You have already assembled parts of this from scattered papers, talks and blog posts. This puts it in one order, with the production layer attached, so the pieces hold together.
By the end you can explain why a model behaves the way it does, fine-tune it for a task your team actually has, serve it behind an API, watch it in production, and say what each choice costs — from prompt engineering through to Kubernetes and SageMaker.
This is a long-form bootcamp and it asks for real hours. Read both lists before you commit any of them.
Reason across the whole stack in one conversation — prompt behaviour, transformer internals, fine-tuning choices, serving, monitoring and the deployment target underneath it.
GenAI & prompt engineering foundations
NLP pipelines and text preprocessing
Classical NLP representations
Deep learning basics for language systems
RNNs, LSTMs, attention & transfer learning
Transformer architecture and attention mechanisms
NER fine-tuning on real downstream tasks
Text generation using beam & greedy search
BERT variants for different tasks
Transformer models in production
Few-label & no-label learning approaches
Current trends in transformer architecture
MLOps foundations for AI systems
Docker, packaging & FastAPI implementation
Model monitoring in production
Kubernetes for ML projects
SageMaker deep dive for GenAI workflows
Agentic AI, RAG, AI Evals and LLMOps are decisions about retrieval quality, model behaviour, latency and cost. Those decisions are made in the language-modelling and production ML layer — which is exactly what you build here.
Scope note. This is not the Agentic AI implementation bootcamp. It is Stage 1 — the foundation you carry into Agentic AI, RAG, LLMOps, AI Evals and production AI engineering.
The whole map, nothing hidden. Expand any section to see exactly what it covers.
Each step assumes the mental model built in the one before it. Nothing here is a detour.
Anchor intuition in what LLMs are actually doing before diving deeper.
Build the vocabulary of preprocessing, tokenization, and classical representations.
Neural nets, activation, training dynamics, RNNs, LSTMs, and transfer learning.
Set up the production mindset before any code ships.
Package models, containerise them, and serve them behind a real API.
Understand how models drift, degrade, and get noticed in production.
Orchestration foundations that show up everywhere in production AI.
Go deep into fine-tuning for downstream tasks and decoding strategy trade-offs.
Understand which variant fits which task — and what that looks like in production.
Close with current architecture trends and a hands-on SageMaker deep dive.
Six choices behind how this is built — all of them about what you can explain afterwards.
Long-form technical content — not tutorial fragments stitched together.
Prompting is one section. The other seventeen build the foundations underneath.
The three foundations of modern language systems, taught in one connected path.
MLOps, Docker, FastAPI, Kubernetes, SageMaker — not left for "later".
Stage 1 of the path — then Production, then Architecture. Nothing here gets relearned later.
Real classroom pacing — walk-throughs, Q&A moments, and concept unfolding.
Slides and source code you can reopen the week a real task lands, instead of re-watching a video to find one command.
Full bootcamp, self-paced access, no cohort date to wait for. You start the day you decide to close the gap.
71+ hours of deep technical content across GenAI, NLP, transformers, deep learning, and production AI workflows — recorded from live bootcamp-style sessions.
Recorded from live bootcamp-style sessions. This is a foundations bootcamp — not the current Agentic AI implementation bootcamp.
Straight answers on scope, depth and fit — before you commit the hours.
No. This is the Generative AI, NLP, transformer and production AI foundation. It is what you carry into Agentic AI, RAG, LLMOps and production AI work — but it is not the Agentic AI implementation bootcamp itself.
The bootcamp includes 71+ hours of recorded content across 18 sections and 54 lectures.
No. Prompt engineering is only one part. The bootcamp also covers NLP, deep learning, transformers, NER fine-tuning, text generation, MLOps, Kubernetes, and SageMaker.
Yes. It includes a deep NLP section with language processing concepts, Word2Vec, TensorFlow references, and transformer updates.
Yes. It includes neural networks, TensorFlow, activation functions, RNNs, LSTMs, transfer learning, and a transformer architecture overview.
Yes. It includes transformer architecture, BERT variants, transformer models in production, and current transformer trends.
Yes. It includes MLOps foundations, version control, Docker, model packaging, FastAPI, monitoring, Kubernetes, and production-oriented workflows.
Yes. It includes a SageMaker deep dive for GenAI workflows.
It assumes you can read code and hold a technical argument. Concepts start from the ground up, but the pace is built for working engineers, not for a first programming course.
Yes. This is a self-paced recorded bootcamp created from live bootcamp-style sessions.
Yes. Agentic AI decisions are model, retrieval, latency and cost decisions. This is where you build the reasoning behind them.
You already deliver with GenAI. Add the model, data and production reasoning behind it, and the next architecture conversation is one you can lead instead of follow.
Go One Layer Down71+ hours · 18 sections · 54 lectures · self-paced recorded bootcamp.
You already bring real engineering experience. These live programs add the production layer on top of it — without asking you to start over.
This course is part of our self-paced foundations library, recorded from earlier live bootcamps. For the current live cohort experience, the programs below are where to go next.
Eight live weeks. One production-style Agentic AI system you build end to end — orchestration, governed tools & MCP, production RAG, async execution, evaluation, security, deployment — and every decision something you can defend. Nothing else required first: Python and LangChain foundation bonuses included free.
Secure Your Seat →Once you can ship the system, the harder question is which system to build. Discovery, scoping, an architecture you can defend, evaluation, delivery and adoption — twelve weeks of live case labs. Reserved for Diamond Members; not sold separately.
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